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eHealth unit HES-SO in Sierre
Henning Müller
Michael Schumacher
eHealth in Sierre
• History:
– Many eHealth projects since 2007
– eHealth unit since 2011
• Applied research, committed to innovation
• Close to user needs, with strong links:
– Locally (Hôpital VS, Logival, …),
– Nationally (CHUV, HUG, EPFL, …) and
– Internationally (Stanford, Harvard, Imperial
College, Carnegie Mellon, NLM, …)
Some of our partners
33333333333333
Some numbers
• 22 collaborators
– 3 professors, 5 engineers, 6 postdocs, 8 PhD students
– Many visiting researchers and exchanges with other
research groups & companies
• 60 peer reviewed publications in 2012
• 1 startup company in 2013
• Projects 2013:
– 8 EU FP7 projects
– 4 FNS + 2 Nano-Tera
– CTI, TheArk, Hasler, …
– Mandates
Research vision
• Medicine is getting increasingly data intensive
– Digital patient is (becoming) a reality
– Health records, Health monitoring, Internet
information, social networks, genomic data, …
• Our main objective is to support the health
domain
– … by connecting data and people
– … understanding and combining multiple data
sources for reliable interpretations
How can we access, use and interpret
data for reliable decision support?
Picture:http://biomedicalcomputationreview.org
Interoperability & Semantics
Picture: http://www.teliris.com
Picture: http://www.teliris.com
Data visualization &
Decision support
Picture: http://www.testroniclabs.com
Health monitoring &
expert systems
Sustainable Health
Technology
• Aging population & lifestyle (diabetes,
cancer, heart diseases, etc.)
• Need to sustain health to change
behavior & to allow for a healthy living
– Shift focus from treatments to detection
and prevention
– Develop early diagnosis & health
monitoring
• Interdisciplinary:
Gestational Diabetes Mellitus
• GDM occurs during pregnancy (4%)
due to increased resistance to insulin
• Goal of the project:
1. Constant monitoring and recording
to ease treatment adjustment
2. Automatic alerts to medical staff
• Technologies:
– Market sensors (glucometers)
– Smart phones & web apps
– Expert systems
• VISual Concept Extraction challenge in
RAdioLogy http://visceral.eu/
• EU funded research project on the creation
of a research infrastructure
– Making big image data sets available for
research in image analysis (10-50 TB)
Organize 2 competitions
• 1. Extract organs and
landmarks in images
– Map these to semantics
– Allow navigation in data
– Basic task required
• 2. Find similar cases
– Including images and
radiology reports
– Combining images, text
and structured data
Our role in VISCERAL
• Create the platform and infrastructure to
manage the research data in the cloud
• Annotate/prepare data
– With radiologists
– Assure interoperability
• Evaluate results
– Assure scalability and automation when
analyzing the data, necessary for big data
• Creation of a gold and silver corpus
– Organize workshops to compare results
Why big data in medicine?
• Data production is already enormous and
it will continue to increase (genetics, …)
– Most can not be used for research as this is
private data
• In very large data similar cases can
always be found
– Learn from the past for the future
– Similar in age, anamnesis, co-morbities
– Also for rare diseases that are currently
problematic
• Clinically-lead EU project, (Children hospital Rome)
• Follows two past projects, health-e-child and
sim-e-child
• Integrate complex data
and support decisions
• Simulate patients and
outcomes
• Avoid animal testing
• http://www.md-paedigree.eu/
Target diseases
• Cardiomyopathies
– Strongly related to imaging
– Simulate treatment outcome
– Personalized care
• Obesity-related cardiovascular
disease
– Strong increase, societal impact
• Juvenile idiopathic arthritis
• Neurological & neuromuscular
diseases
Our role
• Creation of an infostructure to manage all
clinical & research data in the project
– Assure semantic interoperability between the
different clinical partners
– Integrate the data
• Support physicians to find “patients like
mine” and patients to find “patients like me”
– Use structured data, free text and imaging data
combined for similar case retrieval
– Currently analyzing the requirements
Conclusions
• The digital patient is a reality
– Increasingly complex data in large amounts
• Collaboration between all partners in the
health system is required
– Management of big data and use of extracted
information for decision making
• Many technical challenges
– Temporal data, images, semantics
• Sustainable health is the goal of research
More on our research
• Contact:
– Henning.Mueller@hevs.ch
– Michael.Schumacher@hevs.ch
• More information:
– http://publications.hevs.ch/
– http://medgift.hevs.ch/
– http://aislab.hevs.ch/
eHealth unit HES-SO in Sierre

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eHealth unit HES-SO in Sierre

  • 1. eHealth unit HES-SO in Sierre Henning Müller Michael Schumacher
  • 2. eHealth in Sierre • History: – Many eHealth projects since 2007 – eHealth unit since 2011 • Applied research, committed to innovation • Close to user needs, with strong links: – Locally (Hôpital VS, Logival, …), – Nationally (CHUV, HUG, EPFL, …) and – Internationally (Stanford, Harvard, Imperial College, Carnegie Mellon, NLM, …)
  • 3. Some of our partners 33333333333333
  • 4. Some numbers • 22 collaborators – 3 professors, 5 engineers, 6 postdocs, 8 PhD students – Many visiting researchers and exchanges with other research groups & companies • 60 peer reviewed publications in 2012 • 1 startup company in 2013 • Projects 2013: – 8 EU FP7 projects – 4 FNS + 2 Nano-Tera – CTI, TheArk, Hasler, … – Mandates
  • 5. Research vision • Medicine is getting increasingly data intensive – Digital patient is (becoming) a reality – Health records, Health monitoring, Internet information, social networks, genomic data, … • Our main objective is to support the health domain – … by connecting data and people – … understanding and combining multiple data sources for reliable interpretations
  • 6. How can we access, use and interpret data for reliable decision support? Picture:http://biomedicalcomputationreview.org
  • 7.
  • 8. Interoperability & Semantics Picture: http://www.teliris.com
  • 10.
  • 11. Data visualization & Decision support Picture: http://www.testroniclabs.com
  • 12.
  • 14.
  • 15. Sustainable Health Technology • Aging population & lifestyle (diabetes, cancer, heart diseases, etc.) • Need to sustain health to change behavior & to allow for a healthy living – Shift focus from treatments to detection and prevention – Develop early diagnosis & health monitoring • Interdisciplinary:
  • 16. Gestational Diabetes Mellitus • GDM occurs during pregnancy (4%) due to increased resistance to insulin • Goal of the project: 1. Constant monitoring and recording to ease treatment adjustment 2. Automatic alerts to medical staff • Technologies: – Market sensors (glucometers) – Smart phones & web apps – Expert systems
  • 17.
  • 18. • VISual Concept Extraction challenge in RAdioLogy http://visceral.eu/ • EU funded research project on the creation of a research infrastructure – Making big image data sets available for research in image analysis (10-50 TB)
  • 19. Organize 2 competitions • 1. Extract organs and landmarks in images – Map these to semantics – Allow navigation in data – Basic task required • 2. Find similar cases – Including images and radiology reports – Combining images, text and structured data
  • 20. Our role in VISCERAL • Create the platform and infrastructure to manage the research data in the cloud • Annotate/prepare data – With radiologists – Assure interoperability • Evaluate results – Assure scalability and automation when analyzing the data, necessary for big data • Creation of a gold and silver corpus – Organize workshops to compare results
  • 21. Why big data in medicine? • Data production is already enormous and it will continue to increase (genetics, …) – Most can not be used for research as this is private data • In very large data similar cases can always be found – Learn from the past for the future – Similar in age, anamnesis, co-morbities – Also for rare diseases that are currently problematic
  • 22. • Clinically-lead EU project, (Children hospital Rome) • Follows two past projects, health-e-child and sim-e-child • Integrate complex data and support decisions • Simulate patients and outcomes • Avoid animal testing • http://www.md-paedigree.eu/
  • 23. Target diseases • Cardiomyopathies – Strongly related to imaging – Simulate treatment outcome – Personalized care • Obesity-related cardiovascular disease – Strong increase, societal impact • Juvenile idiopathic arthritis • Neurological & neuromuscular diseases
  • 24. Our role • Creation of an infostructure to manage all clinical & research data in the project – Assure semantic interoperability between the different clinical partners – Integrate the data • Support physicians to find “patients like mine” and patients to find “patients like me” – Use structured data, free text and imaging data combined for similar case retrieval – Currently analyzing the requirements
  • 25. Conclusions • The digital patient is a reality – Increasingly complex data in large amounts • Collaboration between all partners in the health system is required – Management of big data and use of extracted information for decision making • Many technical challenges – Temporal data, images, semantics • Sustainable health is the goal of research
  • 26. More on our research • Contact: – Henning.Mueller@hevs.ch – Michael.Schumacher@hevs.ch • More information: – http://publications.hevs.ch/ – http://medgift.hevs.ch/ – http://aislab.hevs.ch/